In addition to one-vs-rest, other methods are applicable
for our scenario [1]}, [2]}, [3]}, [4]}.
Because one-vs-rest does not consider label correlation,
this aspect is the focus of some methods.
For simplicity we
stick with the one-vs-rest setting here
and prioritize achieving good Macro-F1.
Macro-F1 in (REF )
is the average of F1 results over labels,
so under the one-vs-rest framework,
all we need is to design a method that can
give satisfactory F1 on each single label.
In contrast, optimizing Micro-F1 is more
difficult because it couples
all labels and all instances together;
see the definition in (REF ).See, for example, “... is the most challenging measure,
since it does not decompose over instances nor over labels.”
in [5]}
Therefore, we mainly focus on techniques to optimize Macro-F1
in the following sections.
